Reliable dynamical models are required in many important applications such as planning [1]}, model-based reinforcing learning [2]}, system identification [3]}, environment emulation [4]}, SimToReal  [5]}, etc. Moreover, reliably predicting over long-time horizons remains a brutishly difficult problem as compared to a single-step prediction. Such long horizon prediction is particularly useful for downstream planning of robust behaviors [6]}, [7]}, [8]}. Hence, there has been a growing research interest in enhancing the quality of sample-based methods for learning dynamical models [9]}, [10]}, [11]}.
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